PRICE COVID19 Data Report December 2021 Pakistan Registry of Intensive Care
Bibliographic record
Abstract
Abstract Pakistan Registry of Intensive Care (PRICE) is a platform that has enabled standardized COVID-19 clinical data collection based on ISARIC/WHO Clinical Characterization Protocol. The near real-time data platform includes epidemiology, severity of illness, microbiology, treatment and outcomes of patients admitted with suspected or laboratory confirmed COVID19 infection to 67 intensive care and high dependency units across the country. Data has been extracted and analysed at regular intervals to inform stakeholders and improve care practices. This is our 28th report including all patients with suspected or confirmed COVID-19 from 26th March 2020 to 26th December 2021. Key findings from 8624 patients who met eligibility criteria, are as follows: ● Median age of 60 years (IQR 50-70). ● The most common symptoms were shortness of breath (n = 6428, 77.8%), fever (n = 6091, 73.8%), and Cough (n = 3354, 38.9%) ● The most common comorbidity was hypertension followed by diabetes. ● During the course of illness 2804 (32.6%) patients received non-invasive ventilation, whereas 2474 (28.8%) patients had mechanical ventilation as their highest organ support. In addition, 2246 (26.1%) patients needed haemodynamic support and 1249 (14.7%) patients required renal replacement therapy as their highest organ support. ● Median APACHE II score was 18 ● Overall mortality at ICU discharge was 39.2% ● Increasing age and requirement for invasive mechanical ventilation were independent risk factors for mortality increased the risk of death
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".